# this cell is tagged as parameters for `papermill` parameterization
# input configs
altair_config = None
nipah_config = None

# input files
entropy_file = None
func_scores_E2_file = None
binding_E2_file = None
func_scores_E3_file = None
binding_E3_file = None

# output files
filtered_E2_binding_data = None
filtered_E3_binding_data = None
filtered_E2_binding_low_effect = None
filtered_E3_binding_low_effect = None

# output images
entry_binding_combined_corr_plot = None
entry_binding_combined_corr_plot_agg = None
E2_E3_correlation = None
E2_E3_correlation_site = None
combined_E2_E3_site_corr = None
binding_by_site_plot = None
entry_binding_corr_heatmap = None
binding_corr_heatmap = None
binding_region_boxplot_plot = None
binding_region_bubble_plot = None
max_binding_in_stalk = None
max_binding_in_contact = None

# Parameters
nipah_config = "nipah_config.yaml"
altair_config = "data/custom_analyses_data/theme.py"
entropy_file = "results/entropy/entropy.csv"
func_scores_E2_file = "results/func_effects/averages/CHO_EFNB2_low_func_effects.csv"
binding_E2_file = "results/receptor_affinity/averages/EFNB2_monomeric_mut_effect.csv"
func_scores_E3_file = "results/func_effects/averages/CHO_EFNB3_low_func_effects.csv"
binding_E3_file = "results/receptor_affinity/averages/EFNB3_dimeric_mut_effect.csv"
filtered_E2_binding_data = "results/filtered_data/E2_binding_filtered.csv"
filtered_E3_binding_data = "results/filtered_data/E3_binding_filtered.csv"
filtered_E2_binding_low_effect = (
    "results/filtered_data/E2_binding_low_effect_filter.csv"
)
filtered_E3_binding_low_effect = (
    "results/filtered_data/E3_binding_low_effect_filter.csv"
)
entry_binding_combined_corr_plot = (
    "results/images/entry_binding_combined_corr_plot.html"
)
entry_binding_combined_corr_plot_agg = (
    "results/images/entry_binding_combined_corr_plot_agg.html"
)
E2_E3_correlation = "results/images/E2_E3_correlation.html"
E2_E3_correlation_site = "results/images/E2_E3_correlation_site.html"
combined_E2_E3_site_corr = "results/images/combined_E2_E3_site_corr.html"
binding_by_site_plot = "results/images/binding_by_site_plot.html"
entry_binding_corr_heatmap = "results/images/entry_binding_corr_heatmap.html"
binding_corr_heatmap = "results/images/binding_corr_heatmap.html"
binding_region_boxplot_plot = "results/images/binding_region_boxplot_plot.html"
binding_region_bubble_plot = "results/images/binding_region_bubble_plot.html"
max_binding_in_contact = "results/images/max_binding_in_contact.html"
max_binding_in_stalk = "results/images/max_binding_in_stalk.html"

import math
import os
import re
import altair as alt
import numpy as np
import pandas as pd
import scipy.stats
import yaml

# allow more rows for Altair
_ = alt.data_transformers.disable_max_rows()

if (
    os.getcwd()
    == "/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/"
):
    pass
    print("Already in correct directory")
else:
    os.chdir(
        "/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/"
    )
    print("Setup in correct directory")

Setup in correct directory

if nipah_config is None:
    ##hard paths in case don't want to run with snakemake
    print("loading hard paths")
    altair_config = "data/custom_analyses_data/theme.py"
    nipah_config = "nipah_config.yaml"
    entropy_file = "results/entropy/entropy.csv"

    # input files
    func_scores_E2_file = "results/func_effects/averages/CHO_EFNB2_low_func_effects.csv"
    binding_E2_file = (
        "results/receptor_affinity/averages/EFNB2_monomeric_mut_effect.csv"
    )
    func_scores_E3_file = "results/func_effects/averages/CHO_EFNB3_low_func_effects.csv"
    binding_E3_file = "results/receptor_affinity/averages/EFNB3_dimeric_mut_effect.csv"

    filtered_E2_binding_data = "results/filtered_data/E2_binding_filtered.csv"
    filtered_E3_binding_data = "results/filtered_data/E3_binding_filtered.csv"
    filtered_E2_binding_low_effect = (
        "results/filtered_data/E2_binding_low_effect_filter.csv"
    )
    filtered_E3_binding_low_effect = (
        "results/filtered_data/E3_binding_low_effect_filter.csv"
    )

if altair_config:
    with open(altair_config, "r") as file:
        exec(file.read())

with open(nipah_config) as f:
    config = yaml.safe_load(f)

# import binding and entry data
e2 = pd.read_csv(binding_E2_file)
e2_func = pd.read_csv(func_scores_E2_file)
e3 = pd.read_csv(binding_E3_file)
e3_func = pd.read_csv(func_scores_E3_file)

def merge_func_binding_dfs(func, binding, name):
    # Merge the 'binding' and 'func' DataFrames on specified columns with outer join.
    # This combines data based on 'site', 'mutant', and 'wildtype' columns.
    # Suffixes are added to columns with identical names in both DataFrames for differentiation.
    # The result is rounded to three decimal places for neatness.
    df_int = pd.merge(
        binding,
        func,
        on=["site", "mutant", "wildtype"],
        suffixes=["_binding", "_cell_entry"],
        validate="one_to_one",
        how="outer",
    ).round(3)

    # Rename specific columns for clarity.
    df = df_int.rename(
        columns={
            "Ephrin binding_mean": "binding_mean",
            "Ephrin binding_std": "binding_std",
            "Ephrin binding_median": "binding_median",
        }
    )

    # Select and reorder a subset of columns for the final DataFrame.
    df = df[
        [
            "site",
            "wildtype",
            "mutant",
            "binding_mean",
            "binding_std",
            "times_seen_binding",
            "effect",
            "effect_std",
            "times_seen_cell_entry",
            "frac_models",
        ]
    ]

    # Define a function to filter the DataFrame based on several criteria.
    def filter_binding_data(df):
        # Apply multiple conditions to filter the data for relevant entries.
        df_filter = df[
            (df["mutant"] != "*")
            & (df["mutant"] != "-")
            & (df["site"] != 603)
            &
            # Conditions related to cell entry effects and observations.
            (df["effect"] >= config["min_func_effect_for_binding"])
            & (df["times_seen_cell_entry"] >= config["func_times_seen_cutoff"])
            & (df["effect_std"] <= config["func_std_cutoff"])
            &
            # Conditions related to binding observations and variability.
            (df["times_seen_binding"] >= config["min_times_seen_binding"])
            & (df["binding_std"] <= config["max_binding_std"])
            & (df["frac_models"] >= config["frac_models"])
        ]
        return df_filter

    # Filter the DataFrame using the defined function.
    df_filter = filter_binding_data(df)

    # Define a function to identify low effect mutants for further analysis.
    def store_filtered_info(df):
        df_low_filter = df[
            (df["mutant"] != "*")
            & (df["mutant"] != "-")
            & (df["site"] != 603)
            & (df["effect"] < config["min_func_effect_for_binding"])
            & (df["times_seen_cell_entry"] >= config["func_times_seen_cutoff"])
            & (df["effect_std"] <= config["func_std_cutoff"])
        ]
        return df_low_filter

    # Filter the DataFrame to identify low effect mutants.
    df_low_effect_filter = store_filtered_info(df)

    # Save the filtered data to CSV files, differentiating between EFNB2 and others.
    if name == "EFNB2":
        print(name)  # Print the name for confirmation.
        df_filter.to_csv(filtered_E2_binding_data, index=False)
        df_low_effect_filter.to_csv(filtered_E2_binding_low_effect, index=False)
    else:
        df_filter.to_csv(filtered_E3_binding_data, index=False)
        df_low_effect_filter.to_csv(filtered_E3_binding_low_effect, index=False)

    # Return the filtered DataFrames for further use.
    return df_filter, df_low_effect_filter

# Use the defined function to filter data for EFNB2 and EFNB3.
df_E2_filter, df_E2_filter_missing = merge_func_binding_dfs(e2_func, e2, "EFNB2")
df_E3_filter, df_E3_filter_missing = merge_func_binding_dfs(e3_func, e3, "EFNB3")

# Merge the filtered EFNB2 and EFNB3 DataFrames for combined analysis.
df_binding_effect_merge = pd.merge(
    df_E2_filter,
    df_E3_filter,
    on=["site", "wildtype", "mutant"],
    suffixes=["_E2", "_E3"],
    how="outer",
)

# Display descriptive statistics of the merged DataFrame.
display(df_binding_effect_merge.describe().round(3))

# Add a 'selection' column to distinguish between EFNB2 and EFNB3 data.
df_E2_filter["selection"] = "EFNB2"
df_E3_filter["selection"] = "EFNB3"

# Concatenate the DataFrames for plotting or further analysis.
df_binding_effect_concat = pd.concat([df_E2_filter, df_E3_filter])

EFNB2

# What are the top 5 highest and lowest binding mutants for EFNB2 and EFNB3?
def find_highest_lowest(df, name):
    print(f"We are analyzing {name}\n")
    print(f"The total number of mutants was: {df.shape[0]}\n")
    tmp_df = df.sort_values(by="binding_mean")
    print("These are the lowest binding mutants detected:")
    display(tmp_df.head(5))

    tmp_df_high = df.sort_values(by="binding_mean", ascending=False)
    print("\nThese are the highest binding mutants detected:\n")
    display(tmp_df_high.head(5))

    # What about mutants with positive entry scores?
    tmp_df = df[df["effect"] > 0].sort_values(by="binding_mean")
    print("These are the lowest binding mutants detected with positive entry scores:")
    display(tmp_df.head(5))

    tmp_df_high = df[df["effect"] > 0].sort_values(by="binding_mean", ascending=False)
    print(
        "\nThese are the highest binding mutants detected with positive entry scores:\n"
    )
    display(tmp_df_high.head(5))

    blah = df.groupby('site')['binding_mean'].sum().reset_index()
    display(blah.sort_values(by='binding_mean',ascending=False))


find_highest_lowest(df_E2_filter, "EFNB2")
find_highest_lowest(df_E3_filter, "EFNB3")

We are analyzing EFNB2

The total number of mutants was: 6805

These are the lowest binding mutants detected:

These are the highest binding mutants detected:

These are the lowest binding mutants detected with positive entry scores:

These are the highest binding mutants detected with positive entry scores:

We are analyzing EFNB3

The total number of mutants was: 6619

These are the lowest binding mutants detected:

These are the highest binding mutants detected:

These are the lowest binding mutants detected with positive entry scores:

These are the highest binding mutants detected with positive entry scores:

def find_good_binding_for_both(df):
    e2_good = df.sort_values(by='binding_mean_E2',ascending=False)
    e3_good = df.sort_values(by='binding_mean_E3',ascending=False)
    e2_good = e2_good.head(30)
    e3_good = e3_good.head(30)
    combo = pd.merge(e2_good,e3_good,on=['site','wildtype','mutant'],how='inner')
    display(combo)

find_good_binding_for_both(df_binding_effect_merge)

# Compare E2 and E3 binders
def find_highest_lowest(df):
    df["binding_diff"] = (df["binding_mean_E2"] - df["binding_mean_E3"]).abs()
    print(
        "These are the mutants with the biggest difference between EFNB2 and EFNB3:\n"
    )
    display(df.sort_values(by="binding_diff", ascending=False).head(10))

    # calculate aggregate differences
    agg_df = (
        df.groupby("site")[["binding_mean_E2", "binding_mean_E3", "binding_diff"]]
        .median()
        .reset_index()
    )
    print("These are the sites with the biggest difference between EFNB2 and EFNB3:\n")
    display(agg_df.sort_values(by="binding_diff", ascending=False).head(5))


find_highest_lowest(df_binding_effect_merge)
# find_highest_lowest(df_E3_filter,'EFNB3')

These are the mutants with the biggest difference between EFNB2 and EFNB3:

These are the sites with the biggest difference between EFNB2 and EFNB3:

def plot_corr_binding_entry_updated(df, flag):
    # Define interactive selectors for variant selection.
    # 'variant_selector' is for selecting individual variants based on 'site' and 'mutant'.
    variant_selector = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site", "mutant"], value=0
    )
    # 'variant_selector_agg' is for aggregated selection based on 'site' only.
    variant_selector_agg = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site"], value=0
    )

    # Initialize an empty list to store charts for each unique selection in 'df'.
    empty_chart = []
    
    # Loop through each unique cell selection in the DataFrame.
    for cell in list(df["selection"].unique()):
        # Filter DataFrame for the current selection.
        tmp_df = df[df["selection"] == cell]
        
        # Check if aggregation flag is True to aggregate data.
        if flag == True:
            # Aggregate data by 'site', summing 'binding_median' and 'effect', then reset index.
            agg_df = (
                tmp_df.groupby("site")[["binding_mean", "effect"]].sum().reset_index()
            )
            # Create a chart using aggregated data with specified encodings.
            chart = (
                alt.Chart(agg_df)
                .mark_point(stroke="black", filled=True)  # Use filled points with black stroke.
                .encode(
                    x=alt.X(
                        "effect",
                        title=f"Summed {cell} Cell Entry",
                        axis=alt.Axis(grid=True),
                    ),
                    y=alt.Y(
                        "binding_mean",
                        title=f"Summed {cell} Binding",
                        axis=alt.Axis(grid=True),
                    ),
                    # Dynamic opacity, size, strokeWidth, and color based on 'variant_selector_agg'.
                    opacity=alt.condition(
                        variant_selector_agg, alt.value(1), alt.value(0.2)
                    ),
                    size=alt.condition(
                        variant_selector_agg, alt.value(100), alt.value(50)
                    ),
                    strokeWidth=alt.condition(
                        variant_selector_agg, alt.value(1), alt.value(0)
                    ),
                    color=alt.condition(
                        variant_selector_agg, alt.value("orange"), alt.value("black")
                    ),
                    tooltip=["site", "binding_mean", "effect"],
                )
                .add_params(variant_selector_agg)
            )
            # Add the chart to the list.
            empty_chart.append(chart)

        else:
            # Create a chart for individual data points with specified encodings.
            chart = (
                alt.Chart(tmp_df)
                .mark_point(stroke="black", filled=True)
                .encode(
                    x=alt.X(
                        "effect", title=f"{cell} Cell Entry", axis=alt.Axis(grid=True)
                    ),
                    y=alt.Y(
                        "binding_mean",
                        title=f"{cell} Binding",
                        axis=alt.Axis(grid=True),
                    ),
                    # Dynamic opacity, size, strokeWidth, and color based on 'variant_selector'.
                    opacity=alt.condition(
                        variant_selector, alt.value(1), alt.value(0.1)
                    ),
                    size=alt.condition(variant_selector, alt.value(50), alt.value(20)),
                    strokeWidth=alt.condition(
                        variant_selector, alt.value(1), alt.value(0)
                    ),
                    color=alt.condition(
                        variant_selector, alt.value("orange"), alt.value("black")
                    ),
                    tooltip=[
                        "site",
                        "wildtype",
                        "mutant",
                        "binding_mean",
                        "times_seen_binding",
                        "effect",
                    ],
                )
                .add_params(variant_selector)
            )
            # Add the chart to the list.
            empty_chart.append(chart)

    # Combine all charts horizontally with a title.
    combined_chart = alt.hconcat(
        *empty_chart, title=alt.Title("Correlation between binding and entry")
    )
    
    # Return the combined chart for display or further use.
    return combined_chart

# Generate and display plots for non-aggregated data.
entry_binding_corr_plot = plot_corr_binding_entry_updated(df_binding_effect_concat, False)
entry_binding_corr_plot.display()

# Save the plot 
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_plot.save(entry_binding_combined_corr_plot)

# Repeat for aggregated data.
entry_binding_corr_plot_agg = plot_corr_binding_entry_updated(df_binding_effect_concat, True)
entry_binding_corr_plot_agg.display()

# Save the aggregated plot
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_plot_agg.save(entry_binding_combined_corr_plot_agg)

def plot_corr_binding_entry_updated(df, flag):
    # Define interactive selectors for variant selection.
    # 'variant_selector' is for selecting individual variants based on 'site' and 'mutant'.
    variant_selector = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site", "mutant"], value=0
    )
    # 'variant_selector_agg' is for aggregated selection based on 'site' only.
    variant_selector_agg = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site"], value=0
    )

    # Initialize an empty list to store charts for each unique selection in 'df'.
    empty_chart = []
    
    # Loop through each unique cell selection in the DataFrame.
    for cell in list(df["selection"].unique()):
        # Filter DataFrame for the current selection.
        tmp_df = df[df["selection"] == cell]
        
        # Check if aggregation flag is True to aggregate data.
        if flag == True:
            # Aggregate data by 'site', summing 'binding_median' and 'effect', then reset index.
            agg_df = (
                tmp_df.groupby("site")[["binding_mean", "effect"]].sum().reset_index()
            )
            # Create a chart using aggregated data with specified encodings.
            chart = (
                alt.Chart(agg_df)
                .mark_point(stroke="black", filled=True)  # Use filled points with black stroke.
                .encode(
                    x=alt.X(
                        "effect",
                        title=f"Summed {cell} Cell Entry",
                        axis=alt.Axis(grid=True),
                    ),
                    y=alt.Y(
                        "binding_mean",
                        title=f"Summed {cell} Binding",
                        axis=alt.Axis(grid=True),
                    ),
                    # Dynamic opacity, size, strokeWidth, and color based on 'variant_selector_agg'.
                    opacity=alt.condition(
                        variant_selector_agg, alt.value(1), alt.value(0.2)
                    ),
                    size=alt.condition(
                        variant_selector_agg, alt.value(100), alt.value(50)
                    ),
                    strokeWidth=alt.condition(
                        variant_selector_agg, alt.value(1), alt.value(0)
                    ),
                    color=alt.condition(
                        variant_selector_agg, alt.value("orange"), alt.value("black")
                    ),
                    tooltip=["site", "binding_mean", "effect"],
                )
                .add_params(variant_selector_agg)
            )
            # Add the chart to the list.
            empty_chart.append(chart)

        else:
            # Create a chart for individual data points with specified encodings.
            chart = (
                alt.Chart(tmp_df)
                .mark_point(stroke="black", filled=True)
                .encode(
                    x=alt.X(
                        "effect", title=f"{cell} Cell Entry", axis=alt.Axis(grid=True)
                    ),
                    y=alt.Y(
                        "binding_mean",
                        title=f"{cell} Binding",
                        axis=alt.Axis(grid=True),
                    ),
                    # Dynamic opacity, size, strokeWidth, and color based on 'variant_selector'.
                    opacity=alt.condition(
                        variant_selector, alt.value(1), alt.value(0.1)
                    ),
                    size=alt.condition(variant_selector, alt.value(50), alt.value(20)),
                    strokeWidth=alt.condition(
                        variant_selector, alt.value(1), alt.value(0)
                    ),
                    color=alt.condition(
                        variant_selector, alt.value("orange"), alt.value("black")
                    ),
                    tooltip=[
                        "site",
                        "wildtype",
                        "mutant",
                        "binding_mean",
                        "times_seen_binding",
                        "effect",
                    ],
                )
                .add_params(variant_selector)
            )
            # Add the chart to the list.
            empty_chart.append(chart)

    # Combine all charts horizontally with a title.
    combined_chart = alt.hconcat(
        *empty_chart, title=alt.Title("Correlation between binding and entry")
    )
    
    # Return the combined chart for display or further use.
    return combined_chart

# Generate and display plots for non-aggregated data.
entry_binding_corr_plot = plot_corr_binding_entry_updated(df_binding_effect_concat, False)
entry_binding_corr_plot.display()

def plot_entry_binding_corr_heatmap(df):
    empty_chart = []

    for cell in list(df["selection"].unique()):
        tmp_df = df[df["selection"] == cell]
        chart = (
            alt.Chart(tmp_df, title=f"{cell}")
            .mark_rect()
            .encode(
                x=alt.X(
                    "effect", title="Cell Entry", axis=alt.Axis(values=[-2, -1, 0, 1])
                ).bin(maxbins=60),
                y=alt.Y(
                    "binding_mean",
                    title="Binding",
                    axis=alt.Axis(values=[-4, -2, 0, 2]),
                ).bin(maxbins=60),
                color=alt.Color("count()", title="Count").scale(scheme="greenblue"),
            )
        )
        empty_chart.append(chart)

    combined_chart = alt.hconcat(
        *empty_chart, title=alt.Title("Correlation between binding and entry")
    ).resolve_scale(y="shared", x="shared", color="shared")
    return combined_chart


entry_binding_corr_heat = plot_entry_binding_corr_heatmap(df_binding_effect_concat)
entry_binding_corr_heat.display()
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_heat.save(entry_binding_corr_heatmap)

def overall_stats(df, effect, name):
    # Now group sites and find sites where all mutants are deleterious
    filtered_df = df.groupby("site").filter(lambda group: (group[effect] < -0.25).all())
    # Which sites are these?
    unique = filtered_df["site"].unique()
    # Convert unique to a Pandas Series
    unique_series = pd.Series(unique)

    # Find the common elements that are also contact sites
    unique_contact_bool = unique_series.isin(config["contact_sites"])
    # Filter and get the common elements
    common_elements = unique_series[unique_contact_bool]

    print(f"The dataset we are analyzing is: {name}\n")

    # Print the common elements
    print(
        f"Here are the contact sites that only have negative binding scores: {list(common_elements)}\n"
    )

    print(f"There are {len(unique)} sites with all negative binding score mutants\n")
    print(
        f"These are the sites with all negative binding score mutants: {list(unique)}\n"
    )

    # Now find sites with low and high binding (median)
    median_df = (
        df.groupby("site")["binding_mean"]
        .max()
        .reset_index()
        .sort_values(by="binding_mean", ascending=False)
    )
    print("These are the sites with the highest binding mutants:\n")
    display(median_df.head(5))

    # Now calculate mutant number
    total_mutants = df.shape[0]

    mutants_above_cutoff_tolerated = df[df["effect"] > 0]
    mutants_above_cutoff_tolerated = mutants_above_cutoff_tolerated[
        ["site", "effect", "binding_mean", "wildtype", "mutant"]
    ]

    total_sites = df["site"].unique().shape[0]

    print(f"The total number of sites are: {total_sites}")


overall_stats(df_E2_filter, "binding_mean", "EFNB2")
overall_stats(df_E3_filter, "binding_mean", "EFNB3")

The dataset we are analyzing is: EFNB2

Here are the contact sites that only have negative binding scores: [242, 389, 488, 490, 491, 501, 504, 505, 531, 532, 533, 557, 579, 581, 588]

There are 41 sites with all negative binding score mutants

These are the sites with all negative binding score mutants: [100, 116, 220, 236, 238, 242, 243, 248, 346, 351, 352, 389, 390, 400, 435, 438, 441, 460, 467, 486, 487, 488, 490, 491, 494, 495, 497, 501, 504, 505, 526, 531, 532, 533, 557, 579, 581, 584, 585, 588, 590]

These are the sites with the highest binding mutants:

The total number of sites are: 510
The dataset we are analyzing is: EFNB3

Here are the contact sites that only have negative binding scores: [389, 488, 501, 505, 531, 532]

There are 15 sites with all negative binding score mutants

These are the sites with all negative binding score mutants: [108, 352, 389, 467, 486, 488, 494, 495, 497, 501, 505, 510, 531, 532, 584]

These are the sites with the highest binding mutants:

The total number of sites are: 504

# find sites that are different
def find_biggest_differences(df):
    efnb2_good_efnb3_bad = df[
        (df["binding_mean_E2"] > 0.5) & (df["binding_mean_E3"] < -0.5)
    ].sort_values(by="binding_mean_E2", ascending=False)
    display(efnb2_good_efnb3_bad)

    efnb2_bad_efnb3_good = df[
        (df["binding_mean_E2"] < -0.5) & (df["binding_mean_E3"] > 0.5)
    ].sort_values(by="binding_mean_E3", ascending=False)
    display(efnb2_bad_efnb3_good)


find_biggest_differences(df_binding_effect_merge)

def plot_entry_binding_corr(df):
    chart = (
        alt.Chart(df, title="Correlation Between Mutant Binding Scores")
        .mark_rect()
        .encode(
            x=alt.X(
                "binding_mean_E2",
                title="EFNB2 binding",
                axis=alt.Axis(values=[-5, 0, 2]),
            ).bin(maxbins=40),
            y=alt.Y(
                "binding_mean_E3",
                title="EFNB3 binding",
                axis=alt.Axis(values=[-2, 0, 2]),
            ).bin(maxbins=40),
            color=alt.Color("count()", title="Count").scale(scheme="greenblue"),
        )
    )
    return chart


entry_binding_corr_heatmap_1 = plot_entry_binding_corr(df_binding_effect_merge)
entry_binding_corr_heatmap_1.display()
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_heatmap_1.save(binding_corr_heatmap)

def plot_affinity_solid(df):
    df = df.dropna()
    # calculate r value
    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
        df["binding_mean_E2"], df["binding_mean_E3"]
    )
    r_value = float(r_value)
    # make chart
    chart = (
        alt.Chart(
            df,
            title=alt.Title(
                "Correlation between Mutant Binding Scores", subtitle=f"r={r_value:.2f}"
            ),
        )
        .mark_point(color="black", size=30, opacity=0.2, filled=True)
        .encode(
            x=alt.X("binding_mean_E2", title=("EFNB2 Binding")),
            y=alt.Y("binding_mean_E3", title=("EFNB3 Binding")),
            tooltip=[
                "site",
                "wildtype",
                "mutant",
                "binding_mean_E2",
                "binding_mean_E3",
                "effect_E2",
                "effect_E3",
            ],
        )
    )
    min = int(df["binding_mean_E2"].min())
    max = int(df["binding_mean_E3"].max())
    text = (
        alt.Chart({"values": [{"x": min, "y": max, "text": f"r = {r_value:.2f}"}]})
        .mark_text(align="left", baseline="top", dx=-10, dy=-20)
        .encode(x=alt.X("x:Q"), y=alt.Y("y:Q"), text="text:N")
    )
    chart_and_text = chart
    return chart_and_text


E2_E3_corr = plot_affinity_solid(df_binding_effect_merge)
E2_E3_corr.display()
if entry_binding_combined_corr_plot is not None:
    E2_E3_corr.save(E2_E3_correlation)

def plot_affinity_solid_mean(df):
    df = df.dropna()
    means = (
        df.groupby("site")
        .agg(
            {
                "effect_E2": "median",
                "effect_E3": "median",
                "binding_mean_E2": "median",
                "binding_mean_E3": "median",
                "wildtype": "first",
            }
        )
        .reset_index()
    )
    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
        means["binding_mean_E2"], means["binding_mean_E3"]
    )
    r_value = float(r_value)
    chart = (
        alt.Chart(
            means,
            title=alt.Title(
                "Correlation between Aggregate Mutant Binding Scores",
                subtitle=f"r={r_value:.2f}",
            ),
        )
        .mark_point(size=50, opacity=0.3)
        .encode(
            x=alt.X(
                "binding_mean_E2",
                title=("Median EFNB2 Binding"),
                axis=alt.Axis(tickCount=3),
            ),
            y=alt.Y(
                "binding_mean_E3",
                title=("Median EFNB3 Binding"),
                axis=alt.Axis(tickCount=3),
            ),
            tooltip=[
                "site",
                "wildtype",
                "binding_mean_E2",
                "binding_mean_E3",
                "effect_E2",
                "effect_E3",
            ],
        )
    )
    text = (
        alt.Chart({"values": [{"x": -3.5, "y": 0.5, "text": f"r = {r_value:.2f}"}]})
        .mark_text(align="left", baseline="top", dx=0, dy=-10)
        .encode(x=alt.X("x:Q"), y=alt.Y("y:Q"), text="text:N")
    )
    chart_and_text = chart
    return chart_and_text


E2_E3_site_corr = plot_affinity_solid_mean(df_binding_effect_merge)
E2_E3_site_corr.display()
if entry_binding_combined_corr_plot is not None:
    E2_E3_site_corr.save(E2_E3_correlation_site)

if entry_binding_combined_corr_plot is not None:
    (E2_E3_corr | E2_E3_site_corr).save(combined_E2_E3_site_corr)

def plot_affinity_by_site_median(df):
    variant_selector = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site"], value=0
    )
    empty_charts = []
    for selection in ["binding_mean_E2", "binding_mean_E3"]:
        if selection == "binding_mean_E2":
            name = "EFNB2 Binding"
        else:
            name = "EFNB3 Binding"
        mean = df.groupby("site")[selection].max().reset_index()
        mean = mean[mean[selection] >= 0]
        chart = (
            alt.Chart(mean)
            .mark_point(stroke="black", filled=True, size=50)
            .encode(
                x=alt.X(
                    "site",
                    title=("Site"),
                    axis=alt.Axis(grid=True, tickCount=4),
                    scale=alt.Scale(domain=[70, 602]),
                ),
                y=alt.Y(selection, title=(name), axis=alt.Axis(grid=True, tickCount=3)),
                tooltip=["site"],
                color=alt.condition(
                    variant_selector, alt.value("orange"), alt.value("black")
                ),
                opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.5)),
                strokeWidth=alt.condition(variant_selector, alt.value(1), alt.value(0)),
            )
            .properties(height=150, width=500)
            .add_params(variant_selector)
        )
        empty_charts.append(chart)
    combined_chart = alt.vconcat(*empty_charts, spacing=1, title="Max Binding by Site")
    return combined_chart


binding_by_site = plot_affinity_by_site_median(df_binding_effect_merge)
binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
    binding_by_site.save(binding_by_site_plot)

def plot_affinity_by_contact_site(df, sites_to_show, title_text):
    variant_selector = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site"], value=0
    )
    empty_charts = []

    contact_df = df[df["site"].isin(sites_to_show)]
    sites = list(contact_df["site"].unique())

    for selection in df["selection"].unique():
        tmp_df = contact_df[contact_df["selection"] == selection]
        mean = tmp_df.groupby("site")["binding_mean"].max().reset_index()

        chart = (
            alt.Chart(mean)
            .mark_point(size=100)
            .encode(
                x=alt.X(
                    "site:O",
                    sort=sites,
                    title=("Site"),
                    axis=alt.Axis(grid=True, labelAngle=-90),
                    scale=alt.Scale(domain=sites),
                ),
                y=alt.Y(
                    "binding_mean", title=(f"{selection}"), axis=alt.Axis(grid=True)
                ),
                tooltip=["site"],
                color=alt.condition(
                    variant_selector, alt.value("orange"), alt.value("black")
                ),
                strokeWidth=alt.condition(variant_selector, alt.value(2), alt.value(0)),
            )
            .add_params(variant_selector)
        )
        empty_charts.append(chart)
    combined_chart = alt.vconcat(*empty_charts, spacing=1, title=title_text)
    return combined_chart


contact_binding_by_site = plot_affinity_by_contact_site(
    df_binding_effect_concat, config["contact_sites"], "Max Binding in Contact"
)
contact_binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
    contact_binding_by_site.save(max_binding_in_contact)

contact_binding_by_site_stalk = plot_affinity_by_contact_site(
    df_binding_effect_concat, list(range(96, 147)), "Max Binding in Stalk"
)
contact_binding_by_site_stalk.display()
if entry_binding_combined_corr_plot is not None:
    contact_binding_by_site_stalk.save(max_binding_in_stalk)

def make_boxplot_binding_region(
    df, title
):  # Create a box plot using Altair for aggregated means
    barrel_ranges = {
        "Hydrophobic": config["hydrophobic"],
        "Salt Bridges": config["salt_bridges"],
        "Hydrogen Bonds": config["h_bond_total"],
        "Contact": config["contact_sites"],
        "Overall": list(range(71, 602)),
    }

    mean_df = df.groupby("site")[["binding_mean"]].median().reset_index()
    custom_order = [
        "Hydrophobic",
        "Salt Bridges",
        "Hydrogen Bonds",
        "Contact",
        "Overall",
    ]
    agg_means = []

    # For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
    for barrel, sites in barrel_ranges.items():
        subset = mean_df[mean_df["site"].isin(sites)]
        for _, row in subset.iterrows():
            agg_means.append(
                {"barrel": barrel, "effect": row["binding_mean"], "site": row["site"]}
            )
        agg_means_df = pd.DataFrame(agg_means)
    chart = (
        alt.Chart(agg_means_df)
        .mark_point(size=50, opacity=0.4)
        .encode(
            x=alt.X(
                "barrel:O", sort=custom_order, title=None, axis=alt.Axis(labelAngle=-90)
            ),
            y=alt.Y(
                "effect",
                title=f"Median {title} Binding",
                axis=alt.Axis(grid=True, tickCount=4),
            ),
            xOffset="random:Q",
            tooltip=["barrel", "effect", "site"],
        )
        .transform_calculate(random="sqrt(-1*log(random()))*cos(2*PI*random())")
    )

    return chart.display()


make_boxplot_binding_region(df_E2_filter, "EFNB2")
make_boxplot_binding_region(df_E3_filter, "EFNB3")

def make_boxplot_binding_region(df):
    barrel_ranges = {
        "Stalk": list(range(96, 147)),
        "Neck": list(range(148, 165)),
        "Linker": list(range(166, 177)),
        "Head": list(range(178, 602)),
        "Receptor Contact": config["contact_sites"],
        "Total": list(range(71, 602)),
    }
    custom_order = ["Stalk", "Neck", "Linker", "Head", "Receptor Contact", "Total"]
    empty_charts = []
    for selection in df["selection"].unique():
        tmp_df = df[df["selection"] == selection]
        agg_means = []

        # For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
        for barrel, sites in barrel_ranges.items():
            subset = tmp_df[tmp_df["site"].isin(sites)]
            for _, row in subset.iterrows():
                agg_means.append(
                    {
                        "region": barrel,
                        "binding_mean": row["binding_mean"],
                        "site": row["site"],
                    }
                )
            agg_means_df = pd.DataFrame(agg_means)

        chart = (
            alt.Chart(agg_means_df, title=f"{selection}")
            .mark_boxplot(color="darkgray", extent="min-max", opacity=1)
            .encode(
                x=alt.X(
                    "region:O",
                    sort=custom_order,
                    title="RBP Region",
                    axis=alt.Axis(labelAngle=-90),
                ),
                y=alt.Y(
                    "binding_mean",
                    title=f"Binding",
                    axis=alt.Axis(grid=True, tickCount=4),
                ),
                tooltip=["region", "binding_mean", "site"],
            )
            .properties(width=config["width"], height=config["height"])
        )
        empty_charts.append(chart)
    combined_effect_chart = alt.hconcat(*empty_charts).resolve_scale(
        y="shared", x="shared", color="independent"
    )
    return combined_effect_chart


entry_region_boxplot = make_boxplot_binding_region(df_binding_effect_concat)
entry_region_boxplot.display()
if entry_binding_combined_corr_plot is not None:
    entry_region_boxplot.save(binding_region_boxplot_plot)

def make_bubble_binding_region(df):
    barrel_ranges = {
        "Stalk": list(range(96, 147)),
        "Neck": list(range(148, 165)),
        "Linker": list(range(166, 177)),
        "Head": list(range(178, 602)),
        "Receptor Contact": config["contact_sites"],
        "Total": list(range(71, 602)),
    }
    custom_order = ["Stalk", "Neck", "Linker", "Head", "Receptor Contact", "Total"]
    empty_charts = []
    for selection in df["selection"].unique():
        tmp_df = df[df["selection"] == selection]
        agg_means = []

        # For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
        for barrel, sites in barrel_ranges.items():
            subset = tmp_df[tmp_df["site"].isin(sites)]
            for _, row in subset.iterrows():
                agg_means.append(
                    {
                        "region": barrel,
                        "binding_mean": row["binding_mean"],
                        "site": row["site"],
                        "mutant": row["mutant"],
                    }
                )
            agg_means_df = pd.DataFrame(agg_means)

        variant_selector = alt.selection_point(
            on="mouseover", empty=False, fields=["site", "mutant"], value=1
        )

        chart = (
            alt.Chart(agg_means_df, title=f"{selection}")
            .mark_point(opacity=0.3, stroke="black")
            .encode(
                x=alt.X(
                    "region:O",
                    sort=custom_order,
                    title="RBP Region",
                    axis=alt.Axis(labelAngle=-90),
                ),
                y=alt.Y(
                    "binding_mean",
                    title=f"Binding",
                    axis=alt.Axis(grid=True, tickCount=4),
                ),
                xOffset="random:Q",
                tooltip=["region", "binding_mean", "site", "mutant"],
                color=alt.condition(
                    variant_selector, alt.value("orange"), alt.value("black")
                ),
                opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.1)),
                strokeWidth=alt.condition(variant_selector, alt.value(2), alt.value(0)),
                size=alt.condition(variant_selector, alt.value(50), alt.value(15)),
            )
            .transform_calculate(random="sqrt(-1*log(random()))*cos(2*PI*random())")
            .properties(width=config["width"], height=config["height"])
        ).add_params(variant_selector)
        empty_charts.append(chart)
    combined_effect_chart = (
        alt.hconcat(*empty_charts)
        .resolve_scale(y="shared", x="shared", color="independent")
        .add_params(variant_selector)
    )
    return combined_effect_chart


entry_region_bubble = make_bubble_binding_region(df_binding_effect_concat)
entry_region_bubble.display()
if entry_binding_combined_corr_plot is not None:
    entry_region_bubble.save(binding_region_bubble_plot)

	site	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3
count	7319.000	6805.000	6805.000	6805.000	6805.000	6805.000	6805.000	6805.000	6619.000	6619.000	6619.000	6619.000	6619.000	6619.000	6619.0
mean	342.744	-0.315	0.507	6.350	-0.110	0.372	7.631	0.994	-0.018	0.180	6.158	-0.091	0.390	6.733	1.0
std	148.521	1.105	0.340	3.208	0.480	0.195	4.381	0.050	0.272	0.173	3.075	0.447	0.188	3.591	0.0
min	71.000	-5.280	0.005	2.000	-1.498	0.010	2.000	0.500	-2.147	0.000	2.000	-1.499	0.033	2.000	1.0
25%	215.000	-0.448	0.273	4.500	-0.366	0.224	5.000	1.000	-0.150	0.063	4.000	-0.308	0.249	4.571	1.0
50%	341.000	-0.040	0.427	5.750	0.012	0.339	6.750	1.000	-0.012	0.135	5.500	0.029	0.357	6.000	1.0
75%	466.000	0.255	0.651	7.500	0.257	0.485	9.000	1.000	0.118	0.242	7.500	0.236	0.500	7.857	1.0
max	602.000	2.160	2.911	43.750	0.604	0.994	72.380	1.000	2.006	2.957	45.000	0.615	1.000	56.710	1.0

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	selection
8285	501	E	K	-5.280	0.928	24.50	-0.644	0.606	32.00	1.0	EFNB2
8898	532	A	V	-5.259	1.140	16.25	-0.320	0.312	17.88	1.0	EFNB2
3188	236	R	H	-5.054	0.883	22.00	-1.253	0.335	29.38	1.0	EFNB2
8072	490	Q	K	-4.966	1.081	8.50	-0.692	0.539	11.38	1.0	EFNB2
8070	490	Q	H	-4.752	0.862	8.25	-0.450	0.668	10.88	1.0	EFNB2

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	selection
9544	566	F	C	2.160	2.119	5.00	-0.533	0.455	5.500	0.75	EFNB2
9830	580	I	S	2.152	1.768	4.75	-1.001	0.692	6.375	1.00	EFNB2
5007	331	N	E	2.144	1.233	3.00	-0.887	0.844	3.500	1.00	EFNB2
3820	269	N	E	2.075	2.241	5.00	-1.048	0.484	7.875	1.00	EFNB2
9948	586	N	T	1.962	0.578	5.25	-0.816	0.823	7.000	1.00	EFNB2

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	selection
9402	558	A	V	-4.647	0.730	17.25	0.118	0.364	18.50	1.0	EFNB2
9369	557	N	D	-4.627	0.399	9.50	0.385	0.479	11.12	1.0	EFNB2
8773	526	L	H	-4.546	0.782	5.25	0.110	0.392	6.75	1.0	EFNB2
9853	581	Y	W	-4.519	1.013	10.75	0.091	0.410	11.50	1.0	EFNB2
8090	491	S	H	-4.505	0.935	8.50	0.112	0.250	10.25	1.0	EFNB2

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	selection
4080	282	C	S	1.609	1.056	4.50	0.338	0.325	3.714	0.50	EFNB2
7303	450	Q	I	1.457	1.194	5.00	0.142	0.399	6.000	1.00	EFNB2
5159	339	S	A	1.389	2.087	2.00	0.449	0.149	2.625	0.75	EFNB2
5579	361	T	A	1.371	2.108	3.50	0.363	0.450	4.875	1.00	EFNB2
1784	164	N	S	1.297	1.136	3.75	0.240	0.291	6.125	1.00	EFNB2

ephrin_binding¶

hard paths for running in interactive mode¶

Run config files to setup altair theme and config variables¶

Import the binding and entry data for EFNB2 and EFNB3¶

Filter the data and save¶

Find the top overlapping binders for both EFNB2 and EFNB3¶

Make plots showing correlation between binding and entry for EFNB2 and EFNB3¶

Same plot as above, but slightly different format¶

Calculate some stats on binding¶

Find sites with opposite effects on binding¶

Find correlations between EFNB2 and EFNB3 binding¶

Plot correlations between summary statistics for each site¶

Make plot showing binding by site (median)¶

Make bubble plots for binding in different areas of receptor pocket¶

make boxplot of binding scores by region¶

	site	binding_mean
250	331	15.733
473	564	15.087
97	172	13.884
225	306	13.853
493	586	13.370
...	...	...
412	501	-58.053
160	236	-58.154
305	389	-59.775
492	585	-62.244
407	494	-66.568

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	selection
9221	555	D	K	-2.147	1.225	6.0	0.085	0.422	6.714	1.0	EFNB3
9227	555	D	R	-1.510	0.420	4.0	0.076	0.526	4.143	1.0	EFNB3
8739	530	Q	L	-1.447	0.477	3.5	-1.019	0.862	5.857	1.0	EFNB3
9761	583	T	R	-1.410	0.446	5.5	-1.005	0.485	6.286	1.0	EFNB3
9755	583	T	K	-1.247	0.113	5.0	-0.728	0.644	7.000	1.0	EFNB3

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	selection
9866	589	R	G	2.006	0.659	7.0	-0.966	0.576	8.286	1.0	EFNB3
9699	580	I	M	1.337	0.534	5.5	-0.816	0.531	6.714	1.0	EFNB3
3777	270	V	Q	1.329	1.028	5.0	-0.885	0.651	5.429	1.0	EFNB3
3719	267	M	Q	1.291	1.371	4.5	-0.798	0.818	5.667	1.0	EFNB3
8010	492	Q	L	1.261	0.067	5.5	0.483	0.183	5.143	1.0	EFNB3

	site	binding_mean
490	589	11.707
87	161	7.333
469	564	6.722
225	306	6.494
59	132	6.170
...	...	...
270	351	-7.156
165	242	-7.294
465	560	-7.522
162	238	-8.166
461	555	-11.032

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	binding_diff
5836	530	Q	F	-4.015	0.466	6.25	0.467	0.202	5.375	1.0	0.084	0.062	6.0	0.359	0.274	6.286	1.0	4.099
5363	490	Q	Y	-4.290	0.900	5.50	-1.088	0.400	6.375	1.0	-0.461	0.574	5.5	-0.314	0.797	5.714	1.0	3.829
5368	491	S	H	-4.505	0.935	8.50	0.112	0.250	10.250	1.0	-0.872	0.790	7.0	-1.381	0.486	8.286	1.0	3.633
5362	490	Q	W	-4.205	1.005	3.75	0.235	0.378	4.750	1.0	-0.586	0.323	3.0	-0.151	0.458	3.286	1.0	3.619
5367	491	S	G	-4.181	0.595	6.50	0.130	0.298	8.625	1.0	-0.562	0.047	5.5	-0.236	0.608	6.286	1.0	3.619
6628	588	I	P	-2.349	0.840	5.25	-0.256	0.300	4.750	1.0	1.183	0.255	5.5	-0.613	0.382	5.429	1.0	3.532
5386	492	Q	K	-3.523	0.549	11.00	0.353	0.232	11.620	1.0	0.006	0.077	10.0	0.329	0.240	11.570	1.0	3.529
5390	492	Q	R	-2.823	0.611	19.25	-0.077	0.272	23.120	1.0	0.644	0.054	20.0	0.509	0.119	20.570	1.0	3.467
1865	239	S	H	-3.440	0.088	5.25	0.334	0.272	4.500	1.0	-0.041	0.832	4.0	-1.219	0.693	5.000	1.0	3.399
5845	530	Q	V	-4.327	0.838	6.00	0.106	0.269	8.625	1.0	-0.987	0.896	5.5	-1.443	0.739	7.000	1.0	3.340

	site	binding_mean_E2	binding_mean_E3	binding_diff
440	530	-3.8780	-0.6065	3.0575
496	588	-3.6705	-0.2970	3.0010
406	491	-3.7770	-0.4940	2.9905
416	505	-3.8970	-0.7710	2.8970
408	494	-4.0580	-0.6220	2.5440

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	binding_diff
6042	546	L	H	1.523	1.145	5.25	-1.148	0.387	6.750	1.0	-0.543	0.665	3.5	-0.851	0.544	5.143	1.0	2.066
2621	303	P	C	1.345	1.577	5.00	-0.718	0.590	5.000	1.0	-0.566	0.098	4.0	-0.697	0.594	4.857	1.0	1.911
122	79	N	S	1.292	1.652	3.00	-0.635	0.496	2.375	1.0	-0.646	0.260	3.0	-0.129	0.569	3.429	1.0	1.938
103	78	D	I	0.659	0.634	5.25	-0.931	0.398	6.500	1.0	-0.566	0.755	5.0	-0.517	0.845	5.143	1.0	1.225
1682	224	M	Q	0.647	0.880	3.50	0.166	0.315	2.750	1.0	-0.581	0.381	3.5	0.206	0.292	4.286	1.0	1.228
5708	520	I	G	0.591	1.013	5.00	-1.465	0.349	8.000	1.0	-0.530	0.148	5.5	-1.309	0.319	7.000	1.0	1.121
1032	178	V	T	0.529	0.726	3.75	-0.528	0.759	4.250	1.0	-0.513	0.600	3.0	-0.019	0.425	3.571	1.0	1.042

	site	wildtype	mutant	binding_mean_E2_x	binding_std_E2_x	times_seen_binding_E2_x	effect_E2_x	effect_std_E2_x	times_seen_cell_entry_E2_x	frac_models_E2_x	...	effect_std_E2_y	times_seen_cell_entry_E2_y	frac_models_E2_y	binding_mean_E3_y	binding_std_E3_y	times_seen_binding_E3_y	effect_E3_y	effect_std_E3_y	times_seen_cell_entry_E3_y	frac_models_E3_y
0	566	F	C	2.160	2.119	5.00	-0.533	0.455	5.500	0.75	...	0.455	5.500	0.75	1.132	1.194	3.5	-0.851	0.760	3.833	1.0
1	211	G	F	1.957	0.549	4.75	-0.665	0.426	5.125	1.00	...	0.426	5.125	1.00	1.169	0.289	5.0	0.164	0.572	5.714	1.0
2	553	S	W	1.945	0.287	10.00	-0.297	0.429	13.250	1.00	...	0.429	13.250	1.00	0.994	0.435	9.5	0.167	0.406	9.857	1.0
3	139	N	W	1.930	0.483	5.75	-0.667	0.622	8.000	1.00	...	0.622	8.000	1.00	1.003	0.005	4.5	-0.444	0.445	6.286	1.0